Overview

Dataset statistics

Number of variables28
Number of observations1200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory262.6 KiB
Average record size in memory224.1 B

Variable types

Categorical15
Numeric11
Boolean2

Alerts

EmpNumber has a high cardinality: 1200 distinct values High cardinality
Age is highly correlated with TotalWorkExperienceInYearsHigh correlation
EmpJobLevel is highly correlated with TotalWorkExperienceInYearsHigh correlation
TotalWorkExperienceInYears is highly correlated with Age and 2 other fieldsHigh correlation
ExperienceYearsAtThisCompany is highly correlated with TotalWorkExperienceInYears and 3 other fieldsHigh correlation
ExperienceYearsInCurrentRole is highly correlated with ExperienceYearsAtThisCompany and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly correlated with ExperienceYearsAtThisCompany and 1 other fieldsHigh correlation
YearsWithCurrManager is highly correlated with ExperienceYearsAtThisCompany and 1 other fieldsHigh correlation
Age is highly correlated with EmpJobLevel and 1 other fieldsHigh correlation
EmpJobLevel is highly correlated with Age and 2 other fieldsHigh correlation
TotalWorkExperienceInYears is highly correlated with Age and 2 other fieldsHigh correlation
ExperienceYearsAtThisCompany is highly correlated with EmpJobLevel and 4 other fieldsHigh correlation
ExperienceYearsInCurrentRole is highly correlated with ExperienceYearsAtThisCompany and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly correlated with ExperienceYearsAtThisCompany and 1 other fieldsHigh correlation
YearsWithCurrManager is highly correlated with ExperienceYearsAtThisCompany and 1 other fieldsHigh correlation
EmpJobLevel is highly correlated with TotalWorkExperienceInYearsHigh correlation
TotalWorkExperienceInYears is highly correlated with EmpJobLevel and 1 other fieldsHigh correlation
ExperienceYearsAtThisCompany is highly correlated with TotalWorkExperienceInYears and 2 other fieldsHigh correlation
ExperienceYearsInCurrentRole is highly correlated with ExperienceYearsAtThisCompany and 1 other fieldsHigh correlation
YearsWithCurrManager is highly correlated with ExperienceYearsAtThisCompany and 1 other fieldsHigh correlation
EmpJobRole is highly correlated with EmpDepartmentHigh correlation
EmpDepartment is highly correlated with EmpJobRoleHigh correlation
Age is highly correlated with EmpJobLevel and 2 other fieldsHigh correlation
EducationBackground is highly correlated with EmpDepartment and 1 other fieldsHigh correlation
EmpDepartment is highly correlated with EducationBackground and 1 other fieldsHigh correlation
EmpJobRole is highly correlated with EducationBackground and 3 other fieldsHigh correlation
EmpJobLevel is highly correlated with Age and 4 other fieldsHigh correlation
EmpLastSalaryHikePercent is highly correlated with PerformanceRatingHigh correlation
TotalWorkExperienceInYears is highly correlated with Age and 6 other fieldsHigh correlation
ExperienceYearsAtThisCompany is highly correlated with Age and 5 other fieldsHigh correlation
ExperienceYearsInCurrentRole is highly correlated with EmpJobLevel and 4 other fieldsHigh correlation
YearsSinceLastPromotion is highly correlated with TotalWorkExperienceInYears and 3 other fieldsHigh correlation
YearsWithCurrManager is highly correlated with TotalWorkExperienceInYears and 3 other fieldsHigh correlation
PerformanceRating is highly correlated with EmpLastSalaryHikePercentHigh correlation
EmpNumber is uniformly distributed Uniform
EmpNumber has unique values Unique
NumCompaniesWorked has 156 (13.0%) zeros Zeros
TrainingTimesLastYear has 44 (3.7%) zeros Zeros
ExperienceYearsAtThisCompany has 36 (3.0%) zeros Zeros
ExperienceYearsInCurrentRole has 190 (15.8%) zeros Zeros
YearsSinceLastPromotion has 469 (39.1%) zeros Zeros
YearsWithCurrManager has 215 (17.9%) zeros Zeros

Reproduction

Analysis started2021-11-29 20:09:25.631659
Analysis finished2021-11-29 20:09:54.798839
Duration29.17 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

EmpNumber
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct1200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
E100523
 
1
E100350
 
1
E1002164
 
1
E100540
 
1
E100407
 
1
Other values (1195)
1195 

Length

Max length8
Median length8
Mean length7.6375
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1200 ?
Unique (%)100.0%

Sample

1st rowE1001000
2nd rowE1001006
3rd rowE1001007
4th rowE1001009
5th rowE1001010

Common Values

ValueCountFrequency (%)
E1005231
 
0.1%
E1003501
 
0.1%
E10021641
 
0.1%
E1005401
 
0.1%
E1004071
 
0.1%
E10012231
 
0.1%
E10022161
 
0.1%
E1004561
 
0.1%
E1005801
 
0.1%
E10019111
 
0.1%
Other values (1190)1190
99.2%

Length

2021-11-30T01:39:54.907839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e1005231
 
0.1%
e1006691
 
0.1%
e10016611
 
0.1%
e10015791
 
0.1%
e10015341
 
0.1%
e10019341
 
0.1%
e1007591
 
0.1%
e10016861
 
0.1%
e10017131
 
0.1%
e10014991
 
0.1%
Other values (1190)1190
99.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct43
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.91833333
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2021-11-30T01:39:55.070842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.087288534
Coefficient of variation (CV)0.2461456873
Kurtosis-0.4309995808
Mean36.91833333
Median Absolute Deviation (MAD)6
Skewness0.3841449591
Sum44302
Variance82.5788129
MonotonicityNot monotonic
2021-11-30T01:39:55.296842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3471
 
5.9%
3564
 
5.3%
3660
 
5.0%
3157
 
4.8%
2951
 
4.2%
3848
 
4.0%
4046
 
3.8%
3246
 
3.8%
3346
 
3.8%
2743
 
3.6%
Other values (33)668
55.7%
ValueCountFrequency (%)
188
 
0.7%
198
 
0.7%
206
 
0.5%
2111
 
0.9%
2215
 
1.2%
239
 
0.8%
2420
1.7%
2524
2.0%
2633
2.8%
2743
3.6%
ValueCountFrequency (%)
603
 
0.2%
596
 
0.5%
5811
0.9%
574
 
0.3%
5611
0.9%
5517
1.4%
5416
1.3%
5315
1.2%
5215
1.2%
5114
1.2%

Gender
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Male
725 
Female
475 

Length

Max length6
Median length4
Mean length4.791666667
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male725
60.4%
Female475
39.6%

Length

2021-11-30T01:39:55.525845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-30T01:39:55.691845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
male725
60.4%
female475
39.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EducationBackground
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Life Sciences
492 
Medical
384 
Marketing
137 
Technical Degree
100 
Other
66 

Length

Max length16
Median length13
Mean length10.46833333
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarketing
2nd rowMarketing
3rd rowLife Sciences
4th rowHuman Resources
5th rowMarketing

Common Values

ValueCountFrequency (%)
Life Sciences492
41.0%
Medical384
32.0%
Marketing137
 
11.4%
Technical Degree100
 
8.3%
Other66
 
5.5%
Human Resources21
 
1.8%

Length

2021-11-30T01:39:55.856850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-30T01:39:55.986847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
life492
27.1%
sciences492
27.1%
medical384
21.2%
marketing137
 
7.6%
technical100
 
5.5%
degree100
 
5.5%
other66
 
3.6%
human21
 
1.2%
resources21
 
1.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MaritalStatus
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Married
548 
Single
384 
Divorced
268 

Length

Max length8
Median length7
Mean length6.903333333
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowMarried
4th rowDivorced
5th rowSingle

Common Values

ValueCountFrequency (%)
Married548
45.7%
Single384
32.0%
Divorced268
22.3%

Length

2021-11-30T01:39:56.258848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-30T01:39:56.386854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
married548
45.7%
single384
32.0%
divorced268
22.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EmpDepartment
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Sales
373 
Development
361 
Research & Development
343 
Human Resources
54 
Finance
49 

Length

Max length22
Median length11
Mean length12.3125
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales
2nd rowSales
3rd rowSales
4th rowHuman Resources
5th rowSales

Common Values

ValueCountFrequency (%)
Sales373
31.1%
Development361
30.1%
Research & Development343
28.6%
Human Resources54
 
4.5%
Finance49
 
4.1%
Data Science20
 
1.7%

Length

2021-11-30T01:39:56.588849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-30T01:39:56.746850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
development704
35.9%
sales373
19.0%
research343
17.5%
343
17.5%
human54
 
2.8%
resources54
 
2.8%
finance49
 
2.5%
data20
 
1.0%
science20
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EmpJobRole
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct19
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Sales Executive
270 
Developer
236 
Manager R&D
94 
Research Scientist
77 
Sales Representative
69 
Other values (14)
454 

Length

Max length25
Median length15
Mean length14.545
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales Executive
2nd rowSales Executive
3rd rowSales Executive
4th rowManager
5th rowSales Executive

Common Values

ValueCountFrequency (%)
Sales Executive270
22.5%
Developer236
19.7%
Manager R&D94
 
7.8%
Research Scientist77
 
6.4%
Sales Representative69
 
5.8%
Laboratory Technician64
 
5.3%
Senior Developer52
 
4.3%
Manager51
 
4.2%
Finance Manager49
 
4.1%
Human Resources45
 
3.8%
Other values (9)193
16.1%

Length

2021-11-30T01:39:56.984851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sales339
15.9%
developer288
13.5%
executive270
12.7%
manager221
10.4%
r&d109
 
5.1%
representative102
 
4.8%
scientist97
 
4.6%
research96
 
4.5%
senior67
 
3.1%
laboratory64
 
3.0%
Other values (14)475
22.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Travel_Rarely
846 
Travel_Frequently
222 
Non-Travel
132 

Length

Max length17
Median length13
Mean length13.41
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Rarely
3rd rowTravel_Frequently
4th rowTravel_Rarely
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely846
70.5%
Travel_Frequently222
 
18.5%
Non-Travel132
 
11.0%

Length

2021-11-30T01:39:57.183859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-30T01:39:57.312860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely846
70.5%
travel_frequently222
 
18.5%
non-travel132
 
11.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DistanceFromHome
Real number (ℝ≥0)

Distinct29
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.165833333
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2021-11-30T01:39:57.457858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.176636256
Coefficient of variation (CV)0.8920777804
Kurtosis-0.2420167764
Mean9.165833333
Median Absolute Deviation (MAD)5
Skewness0.9629561161
Sum10999
Variance66.85738046
MonotonicityNot monotonic
2021-11-30T01:39:57.618861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2184
15.3%
1170
14.2%
869
 
5.8%
367
 
5.6%
966
 
5.5%
1066
 
5.5%
765
 
5.4%
554
 
4.5%
451
 
4.2%
646
 
3.8%
Other values (19)362
30.2%
ValueCountFrequency (%)
1170
14.2%
2184
15.3%
367
 
5.6%
451
 
4.2%
554
 
4.5%
646
 
3.8%
765
 
5.4%
869
 
5.8%
966
 
5.5%
1066
 
5.5%
ValueCountFrequency (%)
2923
1.9%
2820
1.7%
279
 
0.8%
2622
1.8%
2519
1.6%
2423
1.9%
2322
1.8%
2217
1.4%
2115
1.2%
2019
1.6%
Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
449 
4
322 
2
239 
1
148 
5
 
42

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
3449
37.4%
4322
26.8%
2239
19.9%
1148
 
12.3%
542
 
3.5%

Length

2021-11-30T01:39:57.913860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-30T01:39:58.012862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3449
37.4%
4322
26.8%
2239
19.9%
1148
 
12.3%
542
 
3.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
367 
4
361 
2
242 
1
230 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row2
5th row1

Common Values

ValueCountFrequency (%)
3367
30.6%
4361
30.1%
2242
20.2%
1230
19.2%

Length

2021-11-30T01:39:58.174860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-30T01:39:58.291859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3367
30.6%
4361
30.1%
2242
20.2%
1230
19.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EmpHourlyRate
Real number (ℝ≥0)

Distinct71
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.98166667
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2021-11-30T01:39:58.484863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q383
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)35

Descriptive statistics

Standard deviation20.2113024
Coefficient of variation (CV)0.3063169425
Kurtosis-1.186890513
Mean65.98166667
Median Absolute Deviation (MAD)18
Skewness-0.03516488816
Sum79178
Variance408.4967445
MonotonicityNot monotonic
2021-11-30T01:39:58.685864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7925
 
2.1%
6625
 
2.1%
4224
 
2.0%
5723
 
1.9%
4623
 
1.9%
9222
 
1.8%
9622
 
1.8%
7222
 
1.8%
4522
 
1.8%
4321
 
1.8%
Other values (61)971
80.9%
ValueCountFrequency (%)
3013
1.1%
3113
1.1%
3219
1.6%
3316
1.3%
346
 
0.5%
3514
1.2%
3617
1.4%
3713
1.1%
3810
0.8%
3914
1.2%
ValueCountFrequency (%)
10014
1.2%
9919
1.6%
9820
1.7%
9718
1.5%
9622
1.8%
9517
1.4%
9421
1.8%
9313
1.1%
9222
1.8%
9114
1.2%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
724 
2
294 
4
112 
1
 
70

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3724
60.3%
2294
24.5%
4112
 
9.3%
170
 
5.8%

Length

2021-11-30T01:39:58.892865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-30T01:39:59.004869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3724
60.3%
2294
24.5%
4112
 
9.3%
170
 
5.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EmpJobLevel
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
2
441 
1
440 
3
173 
4
90 
5
56 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row5
5th row2

Common Values

ValueCountFrequency (%)
2441
36.8%
1440
36.7%
3173
 
14.4%
490
 
7.5%
556
 
4.7%

Length

2021-11-30T01:39:59.136870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-30T01:39:59.232866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2441
36.8%
1440
36.7%
3173
 
14.4%
490
 
7.5%
556
 
4.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
4
378 
3
354 
2
237 
1
231 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row1
4th row4
5th row1

Common Values

ValueCountFrequency (%)
4378
31.5%
3354
29.5%
2237
19.8%
1231
19.2%

Length

2021-11-30T01:39:59.371873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-30T01:39:59.459872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
4378
31.5%
3354
29.5%
2237
19.8%
1231
19.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NumCompaniesWorked
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.665
Minimum0
Maximum9
Zeros156
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2021-11-30T01:39:59.575868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.46938384
Coefficient of variation (CV)0.9265980636
Kurtosis0.06886299539
Mean2.665
Median Absolute Deviation (MAD)1
Skewness1.048634755
Sum3198
Variance6.097856547
MonotonicityNot monotonic
2021-11-30T01:39:59.675869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1433
36.1%
0156
 
13.0%
3133
 
11.1%
2123
 
10.2%
4107
 
8.9%
760
 
5.0%
656
 
4.7%
553
 
4.4%
840
 
3.3%
939
 
3.2%
ValueCountFrequency (%)
0156
 
13.0%
1433
36.1%
2123
 
10.2%
3133
 
11.1%
4107
 
8.9%
553
 
4.4%
656
 
4.7%
760
 
5.0%
840
 
3.3%
939
 
3.2%
ValueCountFrequency (%)
939
 
3.2%
840
 
3.3%
760
 
5.0%
656
 
4.7%
553
 
4.4%
4107
 
8.9%
3133
 
11.1%
2123
 
10.2%
1433
36.1%
0156
 
13.0%

OverTime
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
False
847 
True
353 
ValueCountFrequency (%)
False847
70.6%
True353
29.4%
2021-11-30T01:39:59.758869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

EmpLastSalaryHikePercent
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.2225
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2021-11-30T01:39:59.841869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.62591825
Coefficient of variation (CV)0.2381946625
Kurtosis-0.2997407754
Mean15.2225
Median Absolute Deviation (MAD)2
Skewness0.8086536332
Sum18267
Variance13.14728315
MonotonicityNot monotonic
2021-11-30T01:39:59.949875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
14172
14.3%
11169
14.1%
13168
14.0%
12155
12.9%
1582
6.8%
1873
6.1%
1668
 
5.7%
1767
 
5.6%
1963
 
5.2%
2050
 
4.2%
Other values (5)133
11.1%
ValueCountFrequency (%)
11169
14.1%
12155
12.9%
13168
14.0%
14172
14.3%
1582
6.8%
1668
 
5.7%
1767
 
5.6%
1873
6.1%
1963
 
5.2%
2050
 
4.2%
ValueCountFrequency (%)
2513
 
1.1%
2418
 
1.5%
2321
 
1.8%
2247
3.9%
2134
2.8%
2050
4.2%
1963
5.2%
1873
6.1%
1767
5.6%
1668
5.7%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
379 
4
355 
2
247 
1
219 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row3
4th row2
5th row4

Common Values

ValueCountFrequency (%)
3379
31.6%
4355
29.6%
2247
20.6%
1219
18.2%

Length

2021-11-30T01:40:00.100874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-30T01:40:00.191874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3379
31.6%
4355
29.6%
2247
20.6%
1219
18.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TotalWorkExperienceInYears
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct40
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.33
Minimum0
Maximum40
Zeros10
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2021-11-30T01:40:00.323910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.797227967
Coefficient of variation (CV)0.6881931127
Kurtosis0.8056333334
Mean11.33
Median Absolute Deviation (MAD)4
Skewness1.08686186
Sum13596
Variance60.79676397
MonotonicityNot monotonic
2021-11-30T01:40:00.491877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10159
 
13.2%
6105
 
8.8%
885
 
7.1%
977
 
6.4%
571
 
5.9%
165
 
5.4%
761
 
5.1%
451
 
4.2%
1237
 
3.1%
1534
 
2.8%
Other values (30)455
37.9%
ValueCountFrequency (%)
010
 
0.8%
165
5.4%
226
 
2.2%
334
 
2.8%
451
4.2%
571
5.9%
6105
8.8%
761
5.1%
885
7.1%
977
6.4%
ValueCountFrequency (%)
401
 
0.1%
381
 
0.1%
373
 
0.2%
364
0.3%
352
 
0.2%
345
0.4%
337
0.6%
328
0.7%
317
0.6%
305
0.4%

TrainingTimesLastYear
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.785833333
Minimum0
Maximum6
Zeros44
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2021-11-30T01:40:00.662876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.263446168
Coefficient of variation (CV)0.4535253968
Kurtosis0.5675310331
Mean2.785833333
Median Absolute Deviation (MAD)1
Skewness0.5320731986
Sum3343
Variance1.596296219
MonotonicityNot monotonic
2021-11-30T01:40:00.759875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2445
37.1%
3413
34.4%
498
 
8.2%
598
 
8.2%
156
 
4.7%
646
 
3.8%
044
 
3.7%
ValueCountFrequency (%)
044
 
3.7%
156
 
4.7%
2445
37.1%
3413
34.4%
498
 
8.2%
598
 
8.2%
646
 
3.8%
ValueCountFrequency (%)
646
 
3.8%
598
 
8.2%
498
 
8.2%
3413
34.4%
2445
37.1%
156
 
4.7%
044
 
3.7%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
727 
2
294 
4
115 
1
 
64

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3727
60.6%
2294
24.5%
4115
 
9.6%
164
 
5.3%

Length

2021-11-30T01:40:00.895876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-30T01:40:00.988877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3727
60.6%
2294
24.5%
4115
 
9.6%
164
 
5.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ExperienceYearsAtThisCompany
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct37
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0775
Minimum0
Maximum40
Zeros36
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2021-11-30T01:40:01.119882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q310
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.23689903
Coefficient of variation (CV)0.8812291105
Kurtosis4.057959404
Mean7.0775
Median Absolute Deviation (MAD)3
Skewness1.78905498
Sum8493
Variance38.89890951
MonotonicityNot monotonic
2021-11-30T01:40:01.268879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5152
12.7%
1138
11.5%
2107
8.9%
3105
8.8%
10100
8.3%
488
 
7.3%
773
 
6.1%
966
 
5.5%
666
 
5.5%
863
 
5.2%
Other values (27)242
20.2%
ValueCountFrequency (%)
036
 
3.0%
1138
11.5%
2107
8.9%
3105
8.8%
488
7.3%
5152
12.7%
666
5.5%
773
6.1%
863
5.2%
966
5.5%
ValueCountFrequency (%)
401
 
0.1%
371
 
0.1%
362
 
0.2%
341
 
0.1%
335
0.4%
323
0.2%
312
 
0.2%
301
 
0.1%
292
 
0.2%
272
 
0.2%

ExperienceYearsInCurrentRole
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.291666667
Minimum0
Maximum18
Zeros190
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2021-11-30T01:40:01.552883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.613744113
Coefficient of variation (CV)0.8420374631
Kurtosis0.4380286874
Mean4.291666667
Median Absolute Deviation (MAD)3
Skewness0.8881586703
Sum5150
Variance13.05914651
MonotonicityNot monotonic
2021-11-30T01:40:01.681900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2303
25.2%
0190
15.8%
7176
14.7%
3107
 
8.9%
492
 
7.7%
878
 
6.5%
963
 
5.2%
146
 
3.8%
630
 
2.5%
529
 
2.4%
Other values (9)86
 
7.2%
ValueCountFrequency (%)
0190
15.8%
146
 
3.8%
2303
25.2%
3107
 
8.9%
492
 
7.7%
529
 
2.4%
630
 
2.5%
7176
14.7%
878
 
6.5%
963
 
5.2%
ValueCountFrequency (%)
182
 
0.2%
173
 
0.2%
167
 
0.6%
154
 
0.3%
1410
 
0.8%
1310
 
0.8%
127
 
0.6%
1118
 
1.5%
1025
 
2.1%
963
5.2%

YearsSinceLastPromotion
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.194166667
Minimum0
Maximum15
Zeros469
Zeros (%)39.1%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2021-11-30T01:40:01.838881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile10
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.221559873
Coefficient of variation (CV)1.468238453
Kurtosis3.539080079
Mean2.194166667
Median Absolute Deviation (MAD)1
Skewness1.974931559
Sum2633
Variance10.37844801
MonotonicityNot monotonic
2021-11-30T01:40:01.946882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0469
39.1%
1297
24.8%
2127
 
10.6%
762
 
5.2%
453
 
4.4%
345
 
3.8%
535
 
2.9%
624
 
2.0%
1123
 
1.9%
916
 
1.3%
Other values (6)49
 
4.1%
ValueCountFrequency (%)
0469
39.1%
1297
24.8%
2127
 
10.6%
345
 
3.8%
453
 
4.4%
535
 
2.9%
624
 
2.0%
762
 
5.2%
811
 
0.9%
916
 
1.3%
ValueCountFrequency (%)
1511
 
0.9%
145
 
0.4%
138
 
0.7%
129
 
0.8%
1123
 
1.9%
105
 
0.4%
916
 
1.3%
811
 
0.9%
762
5.2%
624
 
2.0%

YearsWithCurrManager
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.105
Minimum0
Maximum17
Zeros215
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2021-11-30T01:40:02.079883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.54157601
Coefficient of variation (CV)0.8627468964
Kurtosis0.1482016446
Mean4.105
Median Absolute Deviation (MAD)3
Skewness0.8131582958
Sum4926
Variance12.54276063
MonotonicityNot monotonic
2021-11-30T01:40:02.196883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2281
23.4%
0215
17.9%
7176
14.7%
3103
 
8.6%
887
 
7.2%
485
 
7.1%
167
 
5.6%
953
 
4.4%
628
 
2.3%
526
 
2.2%
Other values (8)79
 
6.6%
ValueCountFrequency (%)
0215
17.9%
167
 
5.6%
2281
23.4%
3103
 
8.6%
485
 
7.1%
526
 
2.2%
628
 
2.3%
7176
14.7%
887
 
7.2%
953
 
4.4%
ValueCountFrequency (%)
176
 
0.5%
162
 
0.2%
153
 
0.2%
142
 
0.2%
1310
 
0.8%
1214
 
1.2%
1120
 
1.7%
1022
 
1.8%
953
4.4%
887
7.2%

Attrition
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
False
1022 
True
178 
ValueCountFrequency (%)
False1022
85.2%
True178
 
14.8%
2021-11-30T01:40:02.297883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

PerformanceRating
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
874 
2
194 
4
132 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row4
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3874
72.8%
2194
 
16.2%
4132
 
11.0%

Length

2021-11-30T01:40:02.395886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-30T01:40:02.484885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3874
72.8%
2194
 
16.2%
4132
 
11.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

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Correlations

2021-11-30T01:40:02.608885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-30T01:40:03.020888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-30T01:40:03.429894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-30T01:40:03.837896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-11-30T01:40:04.213900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-30T01:39:52.293824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-30T01:39:54.499839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

EmpNumberAgeGenderEducationBackgroundMaritalStatusEmpDepartmentEmpJobRoleBusinessTravelFrequencyDistanceFromHomeEmpEducationLevelEmpEnvironmentSatisfactionEmpHourlyRateEmpJobInvolvementEmpJobLevelEmpJobSatisfactionNumCompaniesWorkedOverTimeEmpLastSalaryHikePercentEmpRelationshipSatisfactionTotalWorkExperienceInYearsTrainingTimesLastYearEmpWorkLifeBalanceExperienceYearsAtThisCompanyExperienceYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAttritionPerformanceRating
0E100100032MaleMarketingSingleSalesSales ExecutiveTravel_Rarely1034553241No124102210708No3
1E100100647MaleMarketingSingleSalesSales ExecutiveTravel_Rarely1444423212No12420237717No3
2E100100740MaleLife SciencesMarriedSalesSales ExecutiveTravel_Frequently544482315Yes21320231813112No4
3E100100941MaleHuman ResourcesDivorcedHuman ResourcesManagerTravel_Rarely1042732543No1522322216126No3
4E100101060MaleMarketingSingleSalesSales ExecutiveTravel_Rarely1641843218No14410132222No3
5E100101127MaleLife SciencesDivorcedDevelopmentDeveloperTravel_Frequently1024323311No2139429717No4
6E100101650MaleMarketingMarriedSalesSales RepresentativeTravel_Rarely844543127No1544232222No3
7E100101928FemaleLife SciencesSingleDevelopmentDeveloperTravel_Rarely121671127Yes13410437737Yes3
8E100102036FemaleLife SciencesMarriedDevelopmentDeveloperNon-Travel831634319No14110238705No3
9E100102138FemaleLife SciencesSingleDevelopmentDeveloperTravel_Rarely133813334Yes14410441000No3

Last rows

EmpNumberAgeGenderEducationBackgroundMaritalStatusEmpDepartmentEmpJobRoleBusinessTravelFrequencyDistanceFromHomeEmpEducationLevelEmpEnvironmentSatisfactionEmpHourlyRateEmpJobInvolvementEmpJobLevelEmpJobSatisfactionNumCompaniesWorkedOverTimeEmpLastSalaryHikePercentEmpRelationshipSatisfactionTotalWorkExperienceInYearsTrainingTimesLastYearEmpWorkLifeBalanceExperienceYearsAtThisCompanyExperienceYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAttritionPerformanceRating
1190E10098323MaleMedicalMarriedDevelopmentDeveloperTravel_Rarely431584112No1615343202No4
1191E10098525MaleLife SciencesMarriedSalesSales ExecutiveTravel_Rarely834574220No2234433212No4
1192E10098738FemaleMarketingSingleSalesSales ExecutiveTravel_Rarely744462240No2018237705No4
1193E10098829MaleLife SciencesDivorcedDevelopmentDeveloperTravel_Frequently142761141No184105310728No3
1194E10099048MaleMarketingMarriedSalesSales ExecutiveTravel_Rarely212564223No12412332222No3
1195E10099227FemaleMedicalDivorcedSalesSales ExecutiveTravel_Frequently314714241Yes2026336504No4
1196E10099337MaleLife SciencesSingleDevelopmentSenior DeveloperTravel_Rarely1024804143No1714231000No3
1197E10099450MaleMedicalMarriedDevelopmentSenior DeveloperTravel_Rarely2814744131Yes113203320838No3
1198E10099534FemaleMedicalSingleData ScienceData ScientistTravel_Rarely934462321No1429348777No3
1199E10099824FemaleLife SciencesSingleSalesSales ExecutiveTravel_Rarely321653239No1414332220Yes2